In complex technical systems it is often difficult to find the precise cause of the fault when malfunctions occur. In a pipe system, for example, the consequence of a leak is that as operation proceeds too small flows are measured and a too severe drop in pressure occurs. Appropriate measuring sensors then raise the alarm, frequently resulting in a flood of messages. Furthermore the repercussions of the fault can appear at some distance from the site where the fault occurred. A further problem is that false alarms can be triggered by incorrect measurements.
Conventionally there are—in addition to a manual analysis of the message flood—approaches for identifying the cause of the fault using neural networks.